pacman::p_load(ggiraph, plotly,
patchwork, DT, tidyverse) Hands-On Exercise 03: Programming Interactive Data Visualisation and Programming Animated Statistical Graphics with R
1. Getting Started with Interactive Data Visualisation
In this exercise, learn about creating interactive data visualisation by using functions provided by ggiraph and plotlyr packages.
The following R packages will be required:
- ggiraph for making ggplot graphics interactive.
- plotly, R library for plotting interactive statistical graphs.
- DT provides an R interface to the JavaScript library DataTables that create interactive table on html page.
- tidyverse, a family of modern R packages specially designed to support data science, analysis and communication task including creating static statistical graphs.
- patchwork for combining multiple ggplot2 graphs into one figure.
To get started, we load the necessary packages into R environment using the code chunk below.
2. Importing Data
The code chunk below uses read_csv() of readr to import the Exam_data dataset into R environment.
exam_data <- read_csv("data/Exam_data.csv")3. Interactive Data Visualisation - ggiraph methods
ggiraph is an htmlwidget and a ggplot2 extension. It allows ggplot graphics to be interactive.
Interactive is made with ggplot geometries that can understand three arguments:
Tooltip: a column of data-sets that contain tooltips to be displayed when the mouse is over elements.
Onclick: a column of data-sets that contain a JavaScript function to be executed when elements are clicked.
Data_id: a column of data-sets that contain an id to be associated with elements.
If it used within a shiny application, elements associated with an id (data_id) can be selected and manipulated on client and server sides.
Note
Refer to this article for more detail explanation.
3.1 Tooltip effect with tooltip aesthetic
Below shows a typical code chunk to plot an interactive statistical graph by using ggiraph package. Notice that the code chunk consists of two parts. First, an ggplot object will be created. Next, girafe() of ggiraph will be used to create an interactive svg object.
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618
)In the first step, an interactive version of ggplot2 geom (i.e. geom_dotplot_interactive()) is used to create the basic graph. Then, girafe() is used to generate an svg object to be displayed on an html page.
4. Interactivity
By hovering the mouse pointer on an data point of interest, the student’s ID will be displayed.
4.1 Displaying multiple information on tooltip
The content of the tooltip can be customised by including a list object as shown in the code chunk below.
exam_data$tooltip <- c(paste0(
"Name = ", exam_data$ID,
"\n Class = ", exam_data$CLASS))
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = exam_data$tooltip),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 8,
height_svg = 8*0.618
)The first three lines of codes in the code chunk create a new field called tooltip. At the same time, it populates text in ID and CLASS fields into the newly created field. Next, this newly created field is used as tooltip field as shown in the code of line 7.
5. Interactivity
By hovering the mouse pointer on an data point of interest, the student’s ID and Class will be displayed.
5.1 Customising Tooltip style
Code chunk below uses opts_tooltip() of ggiraph to customize tooltip rendering by add css declarations.
tooltip_css <- "background-color:white; #<<
font-style:bold; color:black;" #<<
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list( #<<
opts_tooltip( #<<
css = tooltip_css)) #<<
) Notice that the background colour of the tooltip is black and the font colour is white and bold.
Note
Refer to Customizing girafe objects to learn more about how to customise ggiraph objects.
5.2 Displaying statistics on tooltip
Code chunk below shows an advanced way to customise tooltip. In this example, a function is used to compute 90% confident interval of the mean. The derived statistics are then displayed in the tooltip.
tooltip <- function(y, ymax, accuracy = .01) {
mean <- scales::number(y, accuracy = accuracy)
sem <- scales::number(ymax - y, accuracy = accuracy)
paste("Mean maths scores:", mean, "+/-", sem)
}
gg_point <- ggplot(data=exam_data,
aes(x = RACE),
) +
stat_summary(aes(y = MATHS,
tooltip = after_stat(
tooltip(y, ymax))),
fun.data = "mean_se",
geom = GeomInteractiveCol,
fill = "light blue"
) +
stat_summary(aes(y = MATHS),
fun.data = mean_se,
geom = "errorbar", width = 0.2, size = 0.2
)
girafe(ggobj = gg_point,
width_svg = 8,
height_svg = 8*0.618)5.3 Hover effect with data_id aesthetic
Code chunk below shows the second interactive feature of ggiraph, namely data_id.
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618
) Interactivity: Elements associated with a data_id (i.e CLASS) will be highlighted upon mouse over.
Note
The default value of the hover css is hover_css = “fill:orange;”.
5.4 Styling hover effect
In the code chunk below, css codes are used to change the highlighting effect.
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
)Interactivity: Elements associated with a data_id (i.e CLASS) will be highlighted upon mouse over.
Note
Different from previous example, the ccs customisation request in this example are encoded directly.
5.5 Combining tooltip and hover effect
There are times that we want to combine tooltip and hover effect on the interactive statistical graph as shown in the code chunk below.
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = CLASS,
data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
)Interactivity: Elements associated with a data_id (i.e CLASS) will be highlighted upon mouse over. At the same time, the tooltip will show the CLASS.
5.6 Click effect with onclick
onclick argument of ggiraph provides hotlink interactivity on the web.
The code chunk below shown an example of onclick.
Interactivity: Web document link with a data object will be displayed on the web browser upon mouse click.
exam_data$onclick <- sprintf("window.open(\"%s%s\")",
"https://www.moe.gov.sg/schoolfinder?journey=Primary%20school",
as.character(exam_data$ID))
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(onclick = onclick),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618)
Warning
Note that click actions must be a string column in the dataset containing valid javascript instructions.
5.7 Coordinated Multiple Views with ggiraph
Coordinated multiple views methods has been implemented in the data visualisation below.
Notice that when a data point of one of the dotplot is selected, the corresponding data point ID on the second data visualisation will be highlighted too.
In order to build a coordinated multiple views as shown in the example above, the following programming strategy will be used:
Appropriate interactive functions of ggiraph will be used to create the multiple views.
patchwork function of patchwork package will be used inside
girafefunction to create the interactive coordinated multiple views.
p1 <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
coord_cartesian(xlim=c(0,100)) +
scale_y_continuous(NULL,
breaks = NULL)
p2 <- ggplot(data=exam_data,
aes(x = ENGLISH)) +
geom_dotplot_interactive(
aes(data_id = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
coord_cartesian(xlim=c(0,100)) +
scale_y_continuous(NULL,
breaks = NULL)
girafe(code = print(p1 + p2),
width_svg = 6,
height_svg = 3,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
) The data_id aesthetic is critical to link observations between plots and the tooltip aesthetic is optional but nice to have when mouse over a point.
6. Interactive Data Visualisation - plotly methods
Plotly’s R graphing library create interactive web graphics from ggplot2 graphs and/or a custom interface to the (MIT-licensed) JavaScript library plotly.jsinspired by the grammar of graphics. Different from other plotly platform, plot.R is free and open source.
There are two ways to create interactive graph by using plotly, they are:
by using
plot_ly(), andby using
ggplotly()
6.1 Creating an interactive scatter plot: plot_ly() method
The code chunk below shows an example a basic interactive plot created using plot_ly().
plot_ly(data = exam_data,
x = ~MATHS,
y = ~ENGLISH)6.2 Working with visual variable: plot_ly() method
In the code chunk below, color argument is mapped to a qualitative visual variable (i.e. RACE).
plot_ly(data = exam_data,
x = ~ENGLISH,
y = ~MATHS,
color = ~RACE)6.3 Creating an interactive scatter plot: ggplotly() method
The code chunk below plots an interactive scatter plot by using ggplotly().
p <- ggplot(data=exam_data,
aes(x = MATHS,
y = ENGLISH)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
ggplotly(p)
Note
Notice that the only extra line you need to include in the code chunk is ggplotly().
6.4 Coordinated Multiple Views with plotly
The creation of a coordinated linked plot by using plotly involves three steps:
highlight_key()of plotly package is used as shared data.two scatterplots will be created by using ggplot2 functions.
lastly, subplot() of plotly package is used to place them next to each other side-by-side.
d <- highlight_key(exam_data)
p1 <- ggplot(data=d,
aes(x = MATHS,
y = ENGLISH)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
p2 <- ggplot(data=d,
aes(x = MATHS,
y = SCIENCE)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
subplot(ggplotly(p1),
ggplotly(p2))Note:
highlight_key()simply creates an object of class crosstalk::SharedDataVisit this link to learn more about crosstalk
7. Interactive Data Visualisation - crosstalk methods
Crosstalk is an add-on to the htmlwidgets package. It extends htmlwidgets with a set of classes, functions, and conventions for implementing cross-widget interactions (currently, linked brushing and filtering).
7.1 Interactive Data Table: DT package
A wrapper of the JavaScript Library DataTables
Data objects in R can be rendered as HTML tables using the JavaScript library “DataTables” (typically via R Markdown or Shiny).
DT::datatable(exam_data, class= "compact")7.2 Linked brushing: crosstalk method
The code chunk below is used to implement the coordinated brushing.
d <- highlight_key(exam_data)
p <- ggplot(d,
aes(ENGLISH,
MATHS)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
gg <- highlight(ggplotly(p),
"plotly_selected")
crosstalk::bscols(gg,
DT::datatable(d),
widths = 5) 8. Getting Started with Animation
We will learn about creating animated data visualisation:
with gganimate and plotly R packages
reshape data using tidyr package
process, wrangle and transform data with dplyr package
These key concepts are important:
- Frame: In an animated line graph, each frame represents a different point in time or a different category. When the frame changes, the data points on the graph are updated to reflect the new data.
- Animation Attributes: These are settings that control how the animation behaves. For example, you can specify the duration of each frame, the easing function used to transition between frames, and whether to start the animation from the current frame or from the beginning.
Note
If you are conducting an exploratory data analysis, a animated graphic may not be worth the time investment.
However, if you are giving a presentation, a few well-placed animated graphics can help an audience connect with your topic remarkably better than static counterparts.
The following R packages will be required:
plotly, R library for plotting interactive statistical graphs.
gganimate, an ggplot extension for creating animated statistical graphs.
gifski converts video frames to GIF animations using pngquant’s fancy features for efficient cross-frame palettes and temporal dithering. It produces animated GIFs that use thousands of colors per frame.
gapminder: An excerpt of the data available at Gapminder.org. We just want to use its
country_colorsscheme.tidyverse, a family of modern R packages specially designed to support data science, analysis and communication task including creating static statistical graphs.
To get started, we load the necessary packages into R environment using the code chunk below.
pacman::p_load(readxl, gifski, gapminder,
plotly, gganimate, tidyverse)9. Importing Data
The code chunk below uses read_csv() of readr to import the Exam_data dataset into R environment.
col <- c("Country", "Continent")
globalPop <- read_xls("data/GlobalPopulation.xls",
sheet="Data") %>%
mutate_each_(funs(factor(.)), col) %>%
mutate(Year = as.integer(Year))
Note
read_xls()of readxl package is used to import the Excel worksheet.mutate_each_()of dplyr package is used to convert all character data type into factor.mutate_each_()was deprecated in dplyr 0.7.0funs()was deprecated in dplyr 0.8.0Rewrite the code with
mutate_at()
mutateof dplyr package is used to convert data values of Year field into integer.
Re-writing the codes with mutate_at():
col <- c("Country", "Continent")
globalPop <- read_xls("data/GlobalPopulation.xls",
sheet="Data") %>%
mutate_at(col, as.factor) %>%
mutate(Year = as.integer(Year))across() can also be used to derive the same outputs:
col <- c("Country", "Continent")
globalPop <- read_xls("data/GlobalPopulation.xls",
sheet="Data") %>%
mutate(across(col, as.factor)) %>%
mutate(Year = as.integer(Year))10. Animated Data Visualisation: gganimate methods
gganimate extends the grammar of graphics as implemented by ggplot2 to include the description of animation. It does this by providing a range of new grammar classes that can be added to the plot object in order to customise how it should change with time.
transition_*()defines how the data should be spread out and how it relates to itself across time.view_*()defines how the positional scales should change along the animation.shadow_*()defines how data from other points in time should be presented in the given point in time.enter_*()/exit_*()defines how new data should appear and how old data should disappear during the course of the animation.ease_aes()defines how different aesthetics should be eased during transitions.
10.1 Building a static population bubble plot
In the code chunk below, the basic ggplot2 functions are used to create a static bubble plot.
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young') 
10.2 Building the animated bubble plot
In the code chunk below,
transition_time()of gganimate is used to create transition through distinct states in time (i.e. Year).ease_aes()is used to control easing of aesthetics. The default islinear. Other methods are: quadratic, cubic, quartic, quintic, sine, circular, exponential, elastic, back, and bounce.
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young') +
transition_time(Year) +
ease_aes('linear')
11. Animated Data Visualisation: plotly
In Plotly R package, both ggplotly() and plot_ly() support key frame animations through the frame argument/aesthetic. They also support an ids argument/aesthetic to ensure smooth transitions between objects with the same id (which helps facilitate object constancy).
11.1 Building an animated bubble plot: ggplotly() method
Here, we create an animated bubble plot using ggplotly() method, with the following code chunk.
The animated bubble plot above includes a play/pause button and a slider component for controlling the animation.
gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young')
ggplotly(gg)
Note
Appropriate ggplot2 functions are used to create a static bubble plot. The output is then saved as an R object called gg.
ggplotly()is then used to convert the R graphic object into an animated svg object.
Even when the show.legend = FALSE argument was used, the legend still appears on the plot. To overcome this problem, theme(legend.position='none') should be used as shown in the plot and code chunk below.
gg <- ggplot(globalPop,
aes(x = Old,
y = Young,
size = Population,
colour = Country)) +
geom_point(aes(size = Population,
frame = Year),
alpha = 0.7) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(x = '% Aged',
y = '% Young') +
theme(legend.position='none')
ggplotly(gg)11.2 Building an animated bubble plot: plot_ly() method
Here, we create an animated bubble plot using plot_ly() method.
bp <- globalPop %>%
plot_ly(x = ~Old,
y = ~Young,
size = ~Population,
color = ~Continent,
sizes = c(2, 100),
frame = ~Year,
text = ~Country,
hoverinfo = "text",
type = 'scatter',
mode = 'markers'
) %>%
layout(showlegend = FALSE)
bpFootnotes
This document was completed with reference to:
- Prof Kam’s notes “3 Programming Interactive Data Visualisation with R”: https://r4va.netlify.app/chap03
- Prof Kam’s notes “4 Programming Animated Statistical Graphics with R”: https://r4va.netlify.app/chap04